How To Build Your Own Deep Learning Server From Scratch

With tech giants like Microsoft, Google and Amazon providing cloud solutions, why do we need an independent machine or server to do the job? The reason is simple: cloud solutions are far too expensive in the long run and it all depends on the usage of resources.

In this article, we will be concentrating on how to set up the deep learning server, as the components have already been described in one of our former articles. Also, make sure you check out the compatibility of the components.

Here is a list of top performing and affordable hardware components for a Deep Learning Server:

Motherboard: Gigabyte Z370

GPU: GeForce Gtx 1080 Ti

Processor: Intel Core i5 8600K

RAM: Corsair Vengeance LPX 16GB 2x8GB DDR4

Power Supply Unit: Corsair 600W

Now that we have all the components we will start building our Deep Learning Machine.

Choosing The OS

If you come from a software background you will know that a Linux machine can be your best work buddy. We will stick with Ubuntu Server 16.04 LTS as it provides the best support for the softwares that follow.

If you are choosing the Ubuntu Desktop version instead of Ubuntu server you will need to switch to console mode manually.

Making Bootable Disk: This is a fairly simple step. Have your USB stick by your side and follow the simple instructions provided here.

Install the OS with the Bootable USB: This is a straightforward step for anyone who uses a computer fairly. All you need to do is to follow some simple steps. You can find plenty of materials online to get this part done. Restart your system once the installation is complete.

Installing Software Stack for Deep Learning

Before proceeding to install the Nvidia drivers, we must make sure to remove or disable the nouveau driver which is the default driver in Ubuntu. Although we have our GeForce Gtx 1080 Ti inside the box, our machine still uses the inbuilt card that comes with the motherboard.

To activate our graphics card we need to install the drivers.

Installing CUDA

CUDA 10 is the latest version however, we recommend that you stick with the CUDA 9 to avoid dependency issues with cuDNN and Tensorflow

Installing TensorFlow

Follow the instructions here to install TensorFlow in your machine. Make sure to choose the GPU enabled version that is compatible with the versions of CUDA,cuDNN and Ubuntu that we have already installed in our machine.

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A Computer Science Engineer who is passionate about AI and all related technologies. He is someone who loves to stay updated with the Tech-revolutions that AI brings in.
Contact: amal.nair@analyticsindimag.com